Now we test the initialization performance of the proposed approach by the following manner: First, the policy trained by the proposed approach generates trajectories from the initial conditions in the test set. The PTR method then employs these trajectories as an initial guess for trajectory optimization. We choose the PTR method for trajectory optimization since the PTR method has been successfully used in the powered descent guidance applications [1]}, [2]}. We compare the number of iterations and success rate of our method with that of the conventional initialization method that is the straight-line interpolation [3]}. The termination parameters \(\epsilon _{\nu }\)  and \(\epsilon _{tr}\)  are \(10^{-6}\)  and \(10^{-3}\) , respectively, in (REF ). Since the performance of the PTR method depends on the choice of parameters \(w_{\nu }\)  and \(w_{tr}\)  in (REF ), the experiments are carried out with 18 different combinations of these parameters for every initial states, then we record the minimum number of iterations. In the combinations of parameters, \(w_{\nu }\)  take a value in \(\lbrace 10^3,10^4,10^5\rbrace \)  and \(w_{tr}\)  take a value among \(\lbrace 10^{-2},10^{-1},1,10,10^1,10^2,10^3\rbrace \) . The maximum number of iterations in the PTR method is set as 50 since successful convergence of the PTR method usually requires no more than 50 iterations [2]}. If the number of iterations exceeds this maximum, we consider that the optimization has failed to converge.
